EfficientNetV2 models

Original Link : https://keras.io/api/keras_cv/models/backbones/efficientnet_v2/
Last Checked at : 2024-11-25

`EfficientNetV2Backbone` class

keras_cv.models.EfficientNetV2Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_squeeze_and_excite_ratios,
    stackwise_strides,
    stackwise_conv_types,
    skip_connection_dropout=0.2,
    depth_divisor=8,
    min_depth=8,
    activation="swish",
    input_shape=(None, None, 3),
    input_tensor=None,
    **kwargs
)

Instantiates the EfficientNetV2 architecture.

Reference

EfficientNetV2: Smaller Models and Faster Training (ICML 2021)

Arguments

include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
width_coefficient: float, scaling coefficient for network width.
depth_coefficient: float, scaling coefficient for network depth.
stackwise_kernel_sizes: list of ints, the kernel sizes used for each conv block.
stackwise_num_repeats: list of ints, number of times to repeat each conv block.
stackwise_input_filters: list of ints, number of input filters for each conv block.
stackwise_output_filters: list of ints, number of output filters for each stack in the conv blocks model.
stackwise_expansion_ratios: list of floats, expand ratio passed to the squeeze and excitation blocks.
stackwise_squeeze_and_excite_ratios: list of ints, the squeeze and excite ratios passed to the squeeze and excitation blocks.
stackwise_strides: list of ints, stackwise_strides for each conv block.
stackwise_conv_types: list of strings. Each value is either ‘unfused’ or ‘fused’ depending on the desired blocks. FusedMBConvBlock is similar to MBConvBlock, but instead of using a depthwise convolution and a 1x1 output convolution blocks fused blocks use a single 3x3 convolution block.
skip_connection_dropout: float, dropout rate at skip connections.
depth_divisor: integer, a unit of network width.
min_depth: integer, minimum number of filters.
activation: activation function to use between each convolutional layer.
input_shape: optional shape tuple, defaults to (None, None, 3).
input_tensor: optional Keras tensor (i.e. output of keras.layers.Input()) to use as image input for the model.

Example

# Construct an EfficientNetV2 from a preset:
efficientnet = keras_cv.models.EfficientNetV2Backbone.from_preset(
    "efficientnetv2_s"
)
images = tf.ones((1, 256, 256, 3))
outputs = efficientnet.predict(images)
# Alternatively, you can also customize the EfficientNetV2 architecture:
model = EfficientNetV2Backbone(
    stackwise_kernel_sizes=[3, 3, 3, 3, 3, 3],
    stackwise_num_repeats=[2, 4, 4, 6, 9, 15],
    stackwise_input_filters=[24, 24, 48, 64, 128, 160],
    stackwise_output_filters=[24, 48, 64, 128, 160, 256],
    stackwise_expansion_ratios=[1, 4, 4, 4, 6, 6],
    stackwise_squeeze_and_excite_ratios=[0.0, 0.0, 0, 0.25, 0.25, 0.25],
    stackwise_strides=[1, 2, 2, 2, 1, 2],
    stackwise_conv_types=[
        "fused",
        "fused",
        "fused",
        "unfused",
        "unfused",
        "unfused",
    ],
    width_coefficient=1.0,
    depth_coefficient=1.0,
    include_rescaling=False,
)
images = tf.ones((1, 256, 256, 3))
outputs = efficientnet.predict(images)

source

`from_preset` method

EfficientNetV2Backbone.from_preset()

Instantiate EfficientNetV2Backbone model from preset config and weights.

Arguments

preset: string. Must be one of “efficientnetv2_s”, “efficientnetv2_m”, “efficientnetv2_l”, “efficientnetv2_b0”, “efficientnetv2_b1”, “efficientnetv2_b2”, “efficientnetv2_b3”, “efficientnetv2_s_imagenet”, “efficientnetv2_b0_imagenet”, “efficientnetv2_b1_imagenet”, “efficientnetv2_b2_imagenet”. If looking for a preset with pretrained weights, choose one of “efficientnetv2_s_imagenet”, “efficientnetv2_b0_imagenet”, “efficientnetv2_b1_imagenet”, “efficientnetv2_b2_imagenet”.
load_weights: Whether to load pre-trained weights into model. Defaults to None, which follows whether the preset has pretrained weights available.

Examples

# Load architecture and weights from preset
model = keras_cv.models.EfficientNetV2Backbone.from_preset(
    "efficientnetv2_s_imagenet",
)
# Load randomly initialized model from preset architecture with weights
model = keras_cv.models.EfficientNetV2Backbone.from_preset(
    "efficientnetv2_s_imagenet",
    load_weights=False,

Preset name	Parameters	Description
efficientnetv2_s	20.33M	EfficientNet architecture with 6 convolutional blocks.
efficientnetv2_m	53.15M	EfficientNet architecture with 7 convolutional blocks.
efficientnetv2_l	117.75M	EfficientNet architecture with 7 convolutional blocks, but more filters the in `efficientnetv2_m`.
efficientnetv2_b0	5.92M	EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has `width_coefficient=1.0` and `depth_coefficient=1.0`.
efficientnetv2_b1	6.93M	EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has `width_coefficient=1.0` and `depth_coefficient=1.1`.
efficientnetv2_b2	8.77M	EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has `width_coefficient=1.1` and `depth_coefficient=1.2`.
efficientnetv2_b3	12.93M	EfficientNet B-style architecture with 7 convolutional blocks. This B-style model has `width_coefficient=1.2` and `depth_coefficient=1.4`.
efficientnetv2_s_imagenet	20.33M	EfficientNet architecture with 6 convolutional blocks. Weights are initialized to pretrained imagenet classification weights.Published weights are capable of scoring 83.9%top 1 accuracy and 96.7% top 5 accuracy on imagenet.
efficientnetv2_b0_imagenet	5.92M	EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has `width_coefficient=1.0` and `depth_coefficient=1.0`. Weights are initialized to pretrained imagenet classification weights. Published weights are capable of scoring 77.1% top 1 accuracy and 93.3% top 5 accuracy on imagenet.
efficientnetv2_b1_imagenet	6.93M	EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has `width_coefficient=1.0` and `depth_coefficient=1.1`. Weights are initialized to pretrained imagenet classification weights.Published weights are capable of scoring 79.1% top 1 accuracy and 94.4% top 5 accuracy on imagenet.
efficientnetv2_b2_imagenet	8.77M	EfficientNet B-style architecture with 6 convolutional blocks. This B-style model has `width_coefficient=1.1` and `depth_coefficient=1.2`. Weights are initialized to pretrained imagenet classification weights.Published weights are capable of scoring 80.1% top 1 accuracy and 94.9% top 5 accuracy on imagenet.

source

`EfficientNetV2B0Backbone` class

keras_cv.models.EfficientNetV2B0Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_squeeze_and_excite_ratios,
    stackwise_strides,
    stackwise_conv_types,
    skip_connection_dropout=0.2,
    depth_divisor=8,
    min_depth=8,
    activation="swish",
    input_shape=(None, None, 3),
    input_tensor=None,
    **kwargs
)

Instantiates the EfficientNetV2B0 architecture.

Reference

EfficientNetV2: Smaller Models and Faster Training (ICML 2021)

Arguments

include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
input_shape: optional shape tuple, defaults to (None, None, 3).
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

source

`EfficientNetV2B1Backbone` class

keras_cv.models.EfficientNetV2B1Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_squeeze_and_excite_ratios,
    stackwise_strides,
    stackwise_conv_types,
    skip_connection_dropout=0.2,
    depth_divisor=8,
    min_depth=8,
    activation="swish",
    input_shape=(None, None, 3),
    input_tensor=None,
    **kwargs
)

Instantiates the EfficientNetV2B1 architecture.

Reference

EfficientNetV2: Smaller Models and Faster Training (ICML 2021)

Arguments

include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
input_shape: optional shape tuple, defaults to (None, None, 3).
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

source

`EfficientNetV2B2Backbone` class

keras_cv.models.EfficientNetV2B2Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_squeeze_and_excite_ratios,
    stackwise_strides,
    stackwise_conv_types,
    skip_connection_dropout=0.2,
    depth_divisor=8,
    min_depth=8,
    activation="swish",
    input_shape=(None, None, 3),
    input_tensor=None,
    **kwargs
)

Instantiates the EfficientNetV2B2 architecture.

Reference

EfficientNetV2: Smaller Models and Faster Training (ICML 2021)

Arguments

include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
input_shape: optional shape tuple, defaults to (None, None, 3).
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

source

`EfficientNetV2B3Backbone` class

keras_cv.models.EfficientNetV2B3Backbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_squeeze_and_excite_ratios,
    stackwise_strides,
    stackwise_conv_types,
    skip_connection_dropout=0.2,
    depth_divisor=8,
    min_depth=8,
    activation="swish",
    input_shape=(None, None, 3),
    input_tensor=None,
    **kwargs
)

Instantiates the EfficientNetV2B3 architecture.

Reference

EfficientNetV2: Smaller Models and Faster Training (ICML 2021)

Arguments

include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
input_shape: optional shape tuple, defaults to (None, None, 3).
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

source

`EfficientNetV2SBackbone` class

keras_cv.models.EfficientNetV2SBackbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_squeeze_and_excite_ratios,
    stackwise_strides,
    stackwise_conv_types,
    skip_connection_dropout=0.2,
    depth_divisor=8,
    min_depth=8,
    activation="swish",
    input_shape=(None, None, 3),
    input_tensor=None,
    **kwargs
)

Instantiates the EfficientNetV2S architecture.

Reference

EfficientNetV2: Smaller Models and Faster Training (ICML 2021)

Arguments

include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
input_shape: optional shape tuple, defaults to (None, None, 3).
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

source

`EfficientNetV2MBackbone` class

keras_cv.models.EfficientNetV2MBackbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_squeeze_and_excite_ratios,
    stackwise_strides,
    stackwise_conv_types,
    skip_connection_dropout=0.2,
    depth_divisor=8,
    min_depth=8,
    activation="swish",
    input_shape=(None, None, 3),
    input_tensor=None,
    **kwargs
)

Instantiates the EfficientNetV2M architecture.

Reference

EfficientNetV2: Smaller Models and Faster Training (ICML 2021)

Arguments

include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
input_shape: optional shape tuple, defaults to (None, None, 3).
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

source

`EfficientNetV2LBackbone` class

keras_cv.models.EfficientNetV2LBackbone(
    include_rescaling,
    width_coefficient,
    depth_coefficient,
    stackwise_kernel_sizes,
    stackwise_num_repeats,
    stackwise_input_filters,
    stackwise_output_filters,
    stackwise_expansion_ratios,
    stackwise_squeeze_and_excite_ratios,
    stackwise_strides,
    stackwise_conv_types,
    skip_connection_dropout=0.2,
    depth_divisor=8,
    min_depth=8,
    activation="swish",
    input_shape=(None, None, 3),
    input_tensor=None,
    **kwargs
)

Instantiates the EfficientNetV2L architecture.

Reference

EfficientNetV2: Smaller Models and Faster Training (ICML 2021)

Arguments

include_rescaling: bool, whether to rescale the inputs. If set to True, inputs will be passed through a Rescaling(1/255.0) layer.
input_shape: optional shape tuple, defaults to (None, None, 3).
input_tensor: optional Keras tensor (i.e. output of layers.Input()) to use as image input for the model.

EfficientNetV2 models

EfficientNetV2Backbone class

from_preset method

EfficientNetV2B0Backbone class

EfficientNetV2B1Backbone class

EfficientNetV2B2Backbone class

EfficientNetV2B3Backbone class

EfficientNetV2SBackbone class

EfficientNetV2MBackbone class

EfficientNetV2LBackbone class

`EfficientNetV2Backbone` class

`from_preset` method

`EfficientNetV2B0Backbone` class

`EfficientNetV2B1Backbone` class

`EfficientNetV2B2Backbone` class

`EfficientNetV2B3Backbone` class

`EfficientNetV2SBackbone` class

`EfficientNetV2MBackbone` class

`EfficientNetV2LBackbone` class